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Legacy proto-based Types for Google Cloud Bigquery v2 API
WARNING: These types are provided for backward compatibility only, and are not maintained anymore. They might also differ from the types uspported on the backend. It is therefore strongly advised to migrate to the types found in Types for Google Cloud Bigquery v2 API.
Also see the 3.0.0 Migration Guide for more information.
class google.cloud.bigquery_v2.types.DeleteModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
project_id()
Required. Project ID of the model to delete.
Type
dataset_id()
Required. Dataset ID of the model to delete.
Type
model_id()
Required. Model ID of the model to delete.
Type
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.EncryptionConfiguration(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
kms_key_name()
Optional. Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.GetModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
project_id()
Required. Project ID of the requested model.
Type
dataset_id()
Required. Dataset ID of the requested model.
Type
model_id()
Required. Model ID of the requested model.
Type
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.ListModelsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
project_id()
Required. Project ID of the models to list.
Type
dataset_id()
Required. Dataset ID of the models to list.
Type
max_results()
The maximum number of results to return in a single response page. Leverage the page tokens to iterate through the entire collection.
page_token()
Page token, returned by a previous call to request the next page of results
Type
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.ListModelsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
models()
Models in the requested dataset. Only the following fields are populated: model_reference, model_type, creation_time, last_modified_time and labels.
Type
Sequence[google.cloud.bigquery_v2.types.Model]
next_page_token()
A token to request the next page of results.
Type
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.Model(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
etag()
Output only. A hash of this resource.
Type
model_reference()
Required. Unique identifier for this model.
Type
google.cloud.bigquery_v2.types.ModelReference
creation_time()
Output only. The time when this model was created, in millisecs since the epoch.
Type
last_modified_time()
Output only. The time when this model was last modified, in millisecs since the epoch.
Type
description()
Optional. A user-friendly description of this model.
Type
friendly_name()
Optional. A descriptive name for this model.
Type
labels()
The labels associated with this model. You can use these to organize and group your models. Label keys and values can be no longer than 63 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter and each label in the list must have a different key.
expiration_time()
Optional. The time when this model expires, in milliseconds since the epoch. If not present, the model will persist indefinitely. Expired models will be deleted and their storage reclaimed. The defaultTableExpirationMs property of the encapsulating dataset can be used to set a default expirationTime on newly created models.
Type
location()
Output only. The geographic location where the model resides. This value is inherited from the dataset.
Type
encryption_configuration()
Custom encryption configuration (e.g., Cloud KMS keys). This shows the encryption configuration of the model data while stored in BigQuery storage. This field can be used with PatchModel to update encryption key for an already encrypted model.
Type
google.cloud.bigquery_v2.types.EncryptionConfiguration
model_type()
Output only. Type of the model resource.
Type
google.cloud.bigquery_v2.types.Model.ModelType
training_runs()
Output only. Information for all training runs in increasing order of start_time.
Type
Sequence[google.cloud.bigquery_v2.types.Model.TrainingRun]
feature_columns()
Output only. Input feature columns that were used to train this model.
Type
Sequence[google.cloud.bigquery_v2.types.StandardSqlField]
label_columns()
Output only. Label columns that were used to train this model. The output of the model will have a predicted_ prefix to these columns.
Type
Sequence[google.cloud.bigquery_v2.types.StandardSqlField]
best_trial_id()
The best trial_id across all training runs.
Type
class AggregateClassificationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows.
precision()
Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
recall()
Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
accuracy()
Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
threshold()
Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
f1_score()
The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
log_loss()
Logarithmic Loss. For multiclass this is a macro-averaged metric.
roc_auc()
Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class ArimaFittingMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
ARIMA model fitting metrics.
log_likelihood()
Log-likelihood.
Type
aic()
AIC.
Type
variance()
Variance.
Type
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class ArimaForecastingMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Model evaluation metrics for ARIMA forecasting models.
non_seasonal_order()
Non-seasonal order.
Type
Sequence[google.cloud.bigquery_v2.types.Model.ArimaOrder]
arima_fitting_metrics()
Arima model fitting metrics.
Type
Sequence[google.cloud.bigquery_v2.types.Model.ArimaFittingMetrics]
seasonal_periods()
Seasonal periods. Repeated because multiple periods are supported for one time series.
Type
Sequence[google.cloud.bigquery_v2.types.Model.SeasonalPeriod.SeasonalPeriodType]
has_drift()
Whether Arima model fitted with drift or not. It is always false when d is not 1.
Type
Sequence[bool]
time_series_id()
Id to differentiate different time series for the large-scale case.
Type
Sequence[str]
arima_single_model_forecasting_metrics()
Repeated as there can be many metric sets (one for each model) in auto-arima and the large-scale case.
Type
Sequence[google.cloud.bigquery_v2.types.Model.ArimaForecastingMetrics.ArimaSingleModelForecastingMetrics]
class ArimaSingleModelForecastingMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Model evaluation metrics for a single ARIMA forecasting model.
non_seasonal_order()
Non-seasonal order.
Type
google.cloud.bigquery_v2.types.Model.ArimaOrder
arima_fitting_metrics()
Arima fitting metrics.
Type
google.cloud.bigquery_v2.types.Model.ArimaFittingMetrics
has_drift()
Is arima model fitted with drift or not. It is always false when d is not 1.
Type
time_series_id()
The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
Type
time_series_ids()
The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
Type
Sequence[str]
seasonal_periods()
Seasonal periods. Repeated because multiple periods are supported for one time series.
Type
Sequence[google.cloud.bigquery_v2.types.Model.SeasonalPeriod.SeasonalPeriodType]
has_holiday_effect()
If true, holiday_effect is a part of time series decomposition result.
has_spikes_and_dips()
If true, spikes_and_dips is a part of time series decomposition result.
has_step_changes()
If true, step_changes is a part of time series decomposition result.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class ArimaOrder(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Arima order, can be used for both non-seasonal and seasonal parts.
p()
Order of the autoregressive part.
Type
d()
Order of the differencing part.
Type
q()
Order of the moving-average part.
Type
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class BinaryClassificationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Evaluation metrics for binary classification/classifier models.
aggregate_classification_metrics()
Aggregate classification metrics.
Type
google.cloud.bigquery_v2.types.Model.AggregateClassificationMetrics
binary_confusion_matrix_list()
Binary confusion matrix at multiple thresholds.
Type
Sequence[google.cloud.bigquery_v2.types.Model.BinaryClassificationMetrics.BinaryConfusionMatrix]
positive_label()
Label representing the positive class.
Type
negative_label()
Label representing the negative class.
Type
class BinaryConfusionMatrix(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Confusion matrix for binary classification models.
positive_class_threshold()
Threshold value used when computing each of the following metric.
true_positives()
Number of true samples predicted as true.
false_positives()
Number of false samples predicted as true.
true_negatives()
Number of true samples predicted as false.
false_negatives()
Number of false samples predicted as false.
precision()
The fraction of actual positive predictions that had positive actual labels.
recall()
The fraction of actual positive labels that were given a positive prediction.
f1_score()
The equally weighted average of recall and precision.
accuracy()
The fraction of predictions given the correct label.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class ClusteringMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Evaluation metrics for clustering models.
davies_bouldin_index()
Davies-Bouldin index.
mean_squared_distance()
Mean of squared distances between each sample to its cluster centroid.
clusters()
Information for all clusters.
Type
Sequence[google.cloud.bigquery_v2.types.Model.ClusteringMetrics.Cluster]
class Cluster(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Message containing the information about one cluster.
centroid_id()
Centroid id.
Type
feature_values()
Values of highly variant features for this cluster.
Type
Sequence[google.cloud.bigquery_v2.types.Model.ClusteringMetrics.Cluster.FeatureValue]
count()
Count of training data rows that were assigned to this cluster.
class FeatureValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Representative value of a single feature within the cluster.
This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.
feature_column()
The feature column name.
Type
numerical_value()
The numerical feature value. This is the centroid value for this feature.
This field is a member of oneof value
.
categorical_value()
The categorical feature value.
This field is a member of oneof value
.
Type
google.cloud.bigquery_v2.types.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue
class CategoricalValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Representative value of a categorical feature.
category_counts()
Counts of all categories for the categorical feature. If there are more than ten categories, we return top ten (by count) and return one more CategoryCount with category “OTHER” and count as aggregate counts of remaining categories.
Type
Sequence[google.cloud.bigquery_v2.types.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount]
class CategoryCount(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Represents the count of a single category within the cluster.
category()
The name of category.
Type
count()
The count of training samples matching the category within the cluster.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class DataFrequency(value)
Bases: proto.enums.Enum
Type of supported data frequency for time series forecasting models.
AUTO_FREQUENCY( = )
DAILY( = )
DATA_FREQUENCY_UNSPECIFIED( = )
HOURLY( = )
MONTHLY( = )
PER_MINUTE( = )
QUARTERLY( = )
WEEKLY( = )
YEARLY( = )
class DataSplitMethod(value)
Bases: proto.enums.Enum
Indicates the method to split input data into multiple tables.
AUTO_SPLIT( = )
CUSTOM( = )
DATA_SPLIT_METHOD_UNSPECIFIED( = )
NO_SPLIT( = )
RANDOM( = )
SEQUENTIAL( = )
class DataSplitResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Data split result. This contains references to the training and evaluation data tables that were used to train the model.
training_table()
Table reference of the training data after split.
Type
google.cloud.bigquery_v2.types.TableReference
evaluation_table()
Table reference of the evaluation data after split.
Type
google.cloud.bigquery_v2.types.TableReference
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class DistanceType(value)
Bases: proto.enums.Enum
Distance metric used to compute the distance between two points.
COSINE( = )
DISTANCE_TYPE_UNSPECIFIED( = )
EUCLIDEAN( = )
class EvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Evaluation metrics of a model. These are either computed on all training data or just the eval data based on whether eval data was used during training. These are not present for imported models.
This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.
regression_metrics()
Populated for regression models and explicit feedback type matrix factorization models.
This field is a member of oneof metrics
.
Type
google.cloud.bigquery_v2.types.Model.RegressionMetrics
binary_classification_metrics()
Populated for binary classification/classifier models.
This field is a member of oneof metrics
.
Type
google.cloud.bigquery_v2.types.Model.BinaryClassificationMetrics
multi_class_classification_metrics()
Populated for multi-class classification/classifier models.
This field is a member of oneof metrics
.
Type
google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics
clustering_metrics()
Populated for clustering models.
This field is a member of oneof metrics
.
Type
google.cloud.bigquery_v2.types.Model.ClusteringMetrics
ranking_metrics()
Populated for implicit feedback type matrix factorization models.
This field is a member of oneof metrics
.
Type
google.cloud.bigquery_v2.types.Model.RankingMetrics
arima_forecasting_metrics()
Populated for ARIMA models.
This field is a member of oneof metrics
.
Type
google.cloud.bigquery_v2.types.Model.ArimaForecastingMetrics
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class FeedbackType(value)
Bases: proto.enums.Enum
Indicates the training algorithm to use for matrix factorization models.
EXPLICIT( = )
FEEDBACK_TYPE_UNSPECIFIED( = )
IMPLICIT( = )
class GlobalExplanation(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Global explanations containing the top most important features after training.
explanations()
A list of the top global explanations. Sorted by absolute value of attribution in descending order.
Type
Sequence[google.cloud.bigquery_v2.types.Model.GlobalExplanation.Explanation]
class_label()
Class label for this set of global explanations. Will be empty/null for binary logistic and linear regression models. Sorted alphabetically in descending order.
Type
class Explanation(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Explanation for a single feature.
feature_name()
Full name of the feature. For non-numerical features, will be formatted like <column_name>.<encoded_feature_name>. Overall size of feature name will always be truncated to first 120 characters.
Type
attribution()
Attribution of feature.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class HolidayRegion(value)
Bases: proto.enums.Enum
Type of supported holiday regions for time series forecasting models.
AE( = )
AR( = )
AT( = )
AU( = )
BE( = 1 )
BR( = 1 )
CA( = 1 )
CH( = 1 )
CL( = 1 )
CN( = 1 )
CO( = 1 )
CS( = 1 )
CZ( = 1 )
DE( = 1 )
DK( = 2 )
DZ( = 2 )
EC( = 2 )
EE( = 2 )
EG( = 2 )
EMEA( = )
ES( = 2 )
FI( = 2 )
FR( = 2 )
GB( = 2 )
GLOBAL( = )
GR( = 2 )
HK( = 3 )
HOLIDAY_REGION_UNSPECIFIED( = )
HU( = 3 )
ID( = 3 )
IE( = 3 )
IL( = 3 )
IN( = 3 )
IR( = 3 )
IT( = 3 )
JAPAC( = )
JP( = 3 )
KR( = 3 )
LAC( = )
LV( = 4 )
MA( = 4 )
MX( = 4 )
MY( = 4 )
NA( = )
NG( = 4 )
NL( = 4 )
NO( = 4 )
NZ( = 4 )
PE( = 4 )
PH( = 4 )
PK( = 5 )
PL( = 5 )
PT( = 5 )
RO( = 5 )
RS( = 5 )
RU( = 5 )
SA( = 5 )
SE( = 5 )
SG( = 5 )
SI( = 5 )
SK( = 6 )
TH( = 6 )
TR( = 6 )
TW( = 6 )
UA( = 6 )
US( = 6 )
VE( = 6 )
VN( = 6 )
ZA( = 6 )
class KmeansEnums(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
class KmeansInitializationMethod(value)
Bases: proto.enums.Enum
Indicates the method used to initialize the centroids for KMeans clustering algorithm.
CUSTOM( = )
KMEANS_INITIALIZATION_METHOD_UNSPECIFIED( = )
KMEANS_PLUS_PLUS( = )
RANDOM( = )
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class LearnRateStrategy(value)
Bases: proto.enums.Enum
Indicates the learning rate optimization strategy to use.
CONSTANT( = )
LEARN_RATE_STRATEGY_UNSPECIFIED( = )
LINE_SEARCH( = )
class LossType(value)
Bases: proto.enums.Enum
Loss metric to evaluate model training performance.
LOSS_TYPE_UNSPECIFIED( = )
MEAN_LOG_LOSS( = )
MEAN_SQUARED_LOSS( = )
class ModelType(value)
Bases: proto.enums.Enum
Indicates the type of the Model.
ARIMA( = 1 )
ARIMA_PLUS( = 1 )
AUTOML_CLASSIFIER( = 1 )
AUTOML_REGRESSOR( = 1 )
BOOSTED_TREE_CLASSIFIER( = 1 )
BOOSTED_TREE_REGRESSOR( = )
DNN_CLASSIFIER( = )
DNN_REGRESSOR( = )
KMEANS( = )
LINEAR_REGRESSION( = )
LOGISTIC_REGRESSION( = )
MATRIX_FACTORIZATION( = )
MODEL_TYPE_UNSPECIFIED( = )
TENSORFLOW( = )
class MultiClassClassificationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Evaluation metrics for multi-class classification/classifier models.
aggregate_classification_metrics()
Aggregate classification metrics.
Type
google.cloud.bigquery_v2.types.Model.AggregateClassificationMetrics
confusion_matrix_list()
Confusion matrix at different thresholds.
Type
Sequence[google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics.ConfusionMatrix]
class ConfusionMatrix(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Confusion matrix for multi-class classification models.
confidence_threshold()
Confidence threshold used when computing the entries of the confusion matrix.
rows()
One row per actual label.
Type
Sequence[google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics.ConfusionMatrix.Row]
class Entry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
A single entry in the confusion matrix.
predicted_label()
The predicted label. For confidence_threshold > 0, we will also add an entry indicating the number of items under the confidence threshold.
Type
item_count()
Number of items being predicted as this label.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class Row(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
A single row in the confusion matrix.
actual_label()
The original label of this row.
Type
entries()
Info describing predicted label distribution.
Type
Sequence[google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry]
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class OptimizationStrategy(value)
Bases: proto.enums.Enum
Indicates the optimization strategy used for training.
BATCH_GRADIENT_DESCENT( = )
NORMAL_EQUATION( = )
OPTIMIZATION_STRATEGY_UNSPECIFIED( = )
class RankingMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.
mean_average_precision()
Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.
mean_squared_error()
Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.
normalized_discounted_cumulative_gain()
A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.
average_rank()
Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class RegressionMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Evaluation metrics for regression and explicit feedback type matrix factorization models.
mean_absolute_error()
Mean absolute error.
mean_squared_error()
Mean squared error.
mean_squared_log_error()
Mean squared log error.
median_absolute_error()
Median absolute error.
r_squared()
R^2 score. This corresponds to r2_score in ML.EVALUATE.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class SeasonalPeriod(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
class SeasonalPeriodType(value)
Bases: proto.enums.Enum
DAILY( = )
MONTHLY( = )
NO_SEASONALITY( = )
QUARTERLY( = )
SEASONAL_PERIOD_TYPE_UNSPECIFIED( = )
WEEKLY( = )
YEARLY( = )
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class TrainingRun(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Information about a single training query run for the model.
training_options()
Options that were used for this training run, includes user specified and default options that were used.
Type
google.cloud.bigquery_v2.types.Model.TrainingRun.TrainingOptions
start_time()
The start time of this training run.
results()
Output of each iteration run, results.size() <= max_iterations.
Type
Sequence[google.cloud.bigquery_v2.types.Model.TrainingRun.IterationResult]
evaluation_metrics()
The evaluation metrics over training/eval data that were computed at the end of training.
Type
google.cloud.bigquery_v2.types.Model.EvaluationMetrics
data_split_result()
Data split result of the training run. Only set when the input data is actually split.
Type
google.cloud.bigquery_v2.types.Model.DataSplitResult
global_explanations()
Global explanations for important features of the model. For multi-class models, there is one entry for each label class. For other models, there is only one entry in the list.
Type
Sequence[google.cloud.bigquery_v2.types.Model.GlobalExplanation]
class IterationResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Information about a single iteration of the training run.
index()
Index of the iteration, 0 based.
duration_ms()
Time taken to run the iteration in milliseconds.
training_loss()
Loss computed on the training data at the end of iteration.
eval_loss()
Loss computed on the eval data at the end of iteration.
learn_rate()
Learn rate used for this iteration.
Type
cluster_infos()
Information about top clusters for clustering models.
Type
Sequence[google.cloud.bigquery_v2.types.Model.TrainingRun.IterationResult.ClusterInfo]
arima_result()
Type
google.cloud.bigquery_v2.types.Model.TrainingRun.IterationResult.ArimaResult
class ArimaResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
(Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results.
arima_model_info()
This message is repeated because there are multiple arima models fitted in auto-arima. For non-auto-arima model, its size is one.
Type
Sequence[google.cloud.bigquery_v2.types.Model.TrainingRun.IterationResult.ArimaResult.ArimaModelInfo]
seasonal_periods()
Seasonal periods. Repeated because multiple periods are supported for one time series.
Type
Sequence[google.cloud.bigquery_v2.types.Model.SeasonalPeriod.SeasonalPeriodType]
class ArimaCoefficients(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Arima coefficients.
auto_regressive_coefficients()
Auto-regressive coefficients, an array of double.
Type
Sequence[float]
moving_average_coefficients()
Moving-average coefficients, an array of double.
Type
Sequence[float]
intercept_coefficient()
Intercept coefficient, just a double not an array.
Type
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class ArimaModelInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Arima model information.
non_seasonal_order()
Non-seasonal order.
Type
google.cloud.bigquery_v2.types.Model.ArimaOrder
arima_coefficients()
Arima coefficients.
Type
google.cloud.bigquery_v2.types.Model.TrainingRun.IterationResult.ArimaResult.ArimaCoefficients
arima_fitting_metrics()
Arima fitting metrics.
Type
google.cloud.bigquery_v2.types.Model.ArimaFittingMetrics
has_drift()
Whether Arima model fitted with drift or not. It is always false when d is not 1.
Type
time_series_id()
The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
Type
time_series_ids()
The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
Type
Sequence[str]
seasonal_periods()
Seasonal periods. Repeated because multiple periods are supported for one time series.
Type
Sequence[google.cloud.bigquery_v2.types.Model.SeasonalPeriod.SeasonalPeriodType]
has_holiday_effect()
If true, holiday_effect is a part of time series decomposition result.
has_spikes_and_dips()
If true, spikes_and_dips is a part of time series decomposition result.
has_step_changes()
If true, step_changes is a part of time series decomposition result.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class ClusterInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Information about a single cluster for clustering model.
centroid_id()
Centroid id.
Type
cluster_radius()
Cluster radius, the average distance from centroid to each point assigned to the cluster.
cluster_size()
Cluster size, the total number of points assigned to the cluster.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class TrainingOptions(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Options used in model training.
max_iterations()
The maximum number of iterations in training. Used only for iterative training algorithms.
Type
loss_type()
Type of loss function used during training run.
Type
google.cloud.bigquery_v2.types.Model.LossType
learn_rate()
Learning rate in training. Used only for iterative training algorithms.
Type
l1_regularization()
L1 regularization coefficient.
l2_regularization()
L2 regularization coefficient.
min_relative_progress()
When early_stop is true, stops training when accuracy improvement is less than ‘min_relative_progress’. Used only for iterative training algorithms.
warm_start()
Whether to train a model from the last checkpoint.
early_stop()
Whether to stop early when the loss doesn’t improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.
input_label_columns()
Name of input label columns in training data.
Type
Sequence[str]
data_split_method()
The data split type for training and evaluation, e.g. RANDOM.
Type
google.cloud.bigquery_v2.types.Model.DataSplitMethod
data_split_eval_fraction()
The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
Type
data_split_column()
The column to split data with. This column won’t be used as a feature.
When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data.
When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
Type
learn_rate_strategy()
The strategy to determine learn rate for the current iteration.
Type
google.cloud.bigquery_v2.types.Model.LearnRateStrategy
initial_learn_rate()
Specifies the initial learning rate for the line search learn rate strategy.
Type
label_class_weights()
Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.
user_column()
User column specified for matrix factorization models.
Type
item_column()
Item column specified for matrix factorization models.
Type
distance_type()
Distance type for clustering models.
Type
google.cloud.bigquery_v2.types.Model.DistanceType
num_clusters()
Number of clusters for clustering models.
Type
model_uri()
Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
Type
optimization_strategy()
Optimization strategy for training linear regression models.
Type
google.cloud.bigquery_v2.types.Model.OptimizationStrategy
hidden_units()
Hidden units for dnn models.
Type
Sequence[int]
batch_size()
Batch size for dnn models.
Type
dropout()
Dropout probability for dnn models.
max_tree_depth()
Maximum depth of a tree for boosted tree models.
Type
subsample()
Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
Type
min_split_loss()
Minimum split loss for boosted tree models.
num_factors()
Num factors specified for matrix factorization models.
Type
feedback_type()
Feedback type that specifies which algorithm to run for matrix factorization.
Type
google.cloud.bigquery_v2.types.Model.FeedbackType
wals_alpha()
Hyperparameter for matrix factoration when implicit feedback type is specified.
kmeans_initialization_method()
The method used to initialize the centroids for kmeans algorithm.
Type
google.cloud.bigquery_v2.types.Model.KmeansEnums.KmeansInitializationMethod
kmeans_initialization_column()
The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
Type
time_series_timestamp_column()
Column to be designated as time series timestamp for ARIMA model.
Type
time_series_data_column()
Column to be designated as time series data for ARIMA model.
Type
auto_arima()
Whether to enable auto ARIMA or not.
Type
non_seasonal_order()
A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
Type
google.cloud.bigquery_v2.types.Model.ArimaOrder
data_frequency()
The data frequency of a time series.
Type
google.cloud.bigquery_v2.types.Model.DataFrequency
include_drift()
Include drift when fitting an ARIMA model.
Type
holiday_region()
The geographical region based on which the holidays are considered in time series modeling. If a valid value is specified, then holiday effects modeling is enabled.
Type
google.cloud.bigquery_v2.types.Model.HolidayRegion
time_series_id_column()
The time series id column that was used during ARIMA model training.
Type
time_series_id_columns()
The time series id columns that were used during ARIMA model training.
Type
Sequence[str]
horizon()
The number of periods ahead that need to be forecasted.
Type
preserve_input_structs()
Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b.
Type
auto_arima_max_order()
The max value of non-seasonal p and q.
Type
decompose_time_series()
If true, perform decompose time series and save the results.
clean_spikes_and_dips()
If true, clean spikes and dips in the input time series.
adjust_step_changes()
If true, detect step changes and make data adjustment in the input time series.
class LabelClassWeightsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.ModelReference(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Id path of a model.
project_id()
Required. The ID of the project containing this model.
Type
dataset_id()
Required. The ID of the dataset containing this model.
Type
model_id()
Required. The ID of the model. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
Type
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.PatchModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
project_id()
Required. Project ID of the model to patch.
Type
dataset_id()
Required. Dataset ID of the model to patch.
Type
model_id()
Required. Model ID of the model to patch.
Type
model()
Required. Patched model. Follows RFC5789 patch semantics. Missing fields are not updated. To clear a field, explicitly set to default value.
Type
google.cloud.bigquery_v2.types.Model
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.StandardSqlDataType(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
The type of a variable, e.g., a function argument. Examples: INT64: {type_kind=”INT64”} ARRAY: {type_kind=”ARRAY”, array_element_type=”STRING”} STRUCT<x STRING, y ARRAY>: {type_kind=”STRUCT”, struct_type={fields=[ {name=”x”, type={type_kind=”STRING”}}, {name=”y”, type={type_kind=”ARRAY”, array_element_type=”DATE”}} ]}}
This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.
type_kind()
Required. The top level type of this field. Can be any standard SQL data type (e.g., “INT64”, “DATE”, “ARRAY”).
Type
google.cloud.bigquery_v2.types.StandardSqlDataType.TypeKind
array_element_type()
The type of the array’s elements, if type_kind = “ARRAY”.
This field is a member of oneof sub_type
.
Type
google.cloud.bigquery_v2.types.StandardSqlDataType
struct_type()
The fields of this struct, in order, if type_kind = “STRUCT”.
This field is a member of oneof sub_type
.
Type
google.cloud.bigquery_v2.types.StandardSqlStructType
class TypeKind(value)
Bases: proto.enums.Enum
ARRAY( = 1 )
BIGNUMERIC( = 2 )
BOOL( = )
BYTES( = )
DATE( = 1 )
DATETIME( = 2 )
FLOAT64( = )
GEOGRAPHY( = 2 )
INT64( = )
INTERVAL( = 2 )
JSON( = 2 )
NUMERIC( = 2 )
STRING( = )
STRUCT( = 1 )
TIME( = 2 )
TIMESTAMP( = 1 )
TYPE_KIND_UNSPECIFIED( = )
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.StandardSqlField(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
A field or a column.
name()
Optional. The name of this field. Can be absent for struct fields.
Type
type()
Optional. The type of this parameter. Absent if not explicitly specified (e.g., CREATE FUNCTION statement can omit the return type; in this case the output parameter does not have this “type” field).
Type
google.cloud.bigquery_v2.types.StandardSqlDataType
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.StandardSqlStructType(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
fields()
Type
Sequence[google.cloud.bigquery_v2.types.StandardSqlField]
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.StandardSqlTableType(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
A table type
columns()
The columns in this table type
Type
Sequence[google.cloud.bigquery_v2.types.StandardSqlField]
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
class google.cloud.bigquery_v2.types.TableReference(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
project_id()
Required. The ID of the project containing this table.
Type
dataset_id()
Required. The ID of the dataset containing this table.
Type
table_id()
Required. The ID of the table. The ID must contain only
letters (a-z, A-Z), numbers (0-9), or underscores (_). The
maximum length is 1,024 characters. Certain operations allow
suffixing of the table ID with a partition decorator, such
as sample_table$20190123
.
Type
project_id_alternative()
The alternative field that will be used when ESF is not able to translate the received data to the project_id field.
Type
Sequence[str]
dataset_id_alternative()
The alternative field that will be used when ESF is not able to translate the received data to the project_id field.
Type
Sequence[str]
table_id_alternative()
The alternative field that will be used when ESF is not able to translate the received data to the project_id field.
Type
Sequence[str]
_delattr_(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
_eq_(other)
Return True if the messages are equal, False otherwise.
_ne_(other)
Return True if the messages are unequal, False otherwise.
_setattr_(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.